{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_test:  (1000,)\n",
      "X_train:  (49000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214765759e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param) #out: number of sumble, number of filter, out_field_H, out_field_W\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 2e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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UHN4ryAolQgwgc1BHAnI22ScAMmhjNeBchzhF1eE8aHQ2Y6dHbNIrwKDLrMqQvgCRAL0m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M1Erz0fZRLyuYBiMU8h5+NrnRJ0yuAFPoE3Xzwx5Ap5myahUCikgQAwB3nz9B0OmxBOppgk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\n3Pz8PB6PRxUl3t3dJZvNUiwW6Xa7DA8Po+u6Eh+7RcfRaJSdnR0Vo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DzMysqKYsiFiZDw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5fv06zWaz22m747A+MjJCrVZjbGwMv9+P3W5Xm+vVq1f5yle+wsLCAoFAgL/8y79UCIy0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\nGPQ62q0OQb8fPaA3tKnTNVFt5gsEXA7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6nSpQSUJmNBo5f/48165dw2w2K2pM7ETk63D4\nzZ0OcTMKcq/4Xltoa82J4fUZonu7IvfGjN0FPCreCPoEqEAtAb5YLKp9VmhmnU5HMBhUwnKhMIVm\nFOF4MplUti1Cu4k+0uv14nQ61eQHoaFdLhczMzNEIhGFJvf395PP5wkGg4yPj7OwsMC5c+cYHBxU\nxYPoHBOJhLKRMJvNhMNhVRjIe5IkRc6DJCxOp1P5hImmUqhsvb47e1J7HaSBRdadjEmC1+9pq9Wq\nkDVB4eS5gkBLV7YgVUL5bmxsKCNnuUaSAMucRykqhdKUhFioSEHGc7mcQqsHBgbI5/PM74y+0+6r\ncm99+tOf/pdPQWpXVbvTQb+zEAw71bw2qEilLmNTYPcJka8l8+50Xm/HV/y7sVvdtao7YyWcRqxm\nE+7+AZK5bdxeJ4V0Cs/+AcydMuPODhuRda7ETVwJJ3D5A1yZv4bNYGB6/wEya1ssrG5icfq5sbSJ\nxZFmu5Cnf3yKVi3NkYlJIok0nqAbo1GP22ZmaXmZVDSO02hi5tZDPHvxAoNjkwSDPTz57ScI+oNY\nmy3KLrixsMSJ9z3Ep37hX3Hhuy/wy//Xp4nFInz5C5/DYrOyur7Gb//mb/E3X/oSnVab6QMH2NfT\nw9f/4XHe/e53k0qlqNVqFIpF2rSUW/j2doxGrcrQ0BDLSytMTEywtLqCwWQk2BPCFwoSCoVIbacY\nHhrCYDAwO9vt/picnPz/uHvTGMvu88zvd5a77/utvbuqurq6yV7IFkmJFC3SsqjFGnngGVuxAyVA\nJsH4yyQzH+aLESeKHBhB4Mkg42A8IxjOILElGDPxRJBCa4tNaiXFJtnNbvZWXV37fvd9O+fkw6n3\n36dKRVq2lFFjDlCo7d5z7z3nvzzv8z7v83Lj5nV2t3fIZDI8efkyjuOwunwPzYF//vu/Rzzudryv\nHkZLH/jABxgbG2NvY4Ov/Lt/y8WLF3nxFz7Cy9/8Fn3LRmLCBw8esL25AbZNt9Mh4tOJBQz8kSB+\nv4+Dgz0KhTHymSyW5bjVa70+lVqNeCSOPxagPRxiOBpWv4+FhuW4IG1rf5f9g13CQZNWo04sEiZo\nptB1MOJu/79+YMijcpwkppdN1ath8AqHvanH48GMBCre8noBX162Rs4djUZVhVC321XaCzEqBZTw\n9cGDB4oxa7VarKysoGka1WpVVV099dRTKpXp9/uV39Da2hqVSkX1ssvn8yo1GI/HuXfvHolEQjlj\ni2njF7/4Rb70pS9x7tw5FhYWVLn57u4u8/Pz/OAHPzhiRrq+vs7c3JwCKlL6f3BwoEDUYDAgkUjQ\naDRUG6N2u00ymSSXy6nqUDFzXV5eVm19hCFIpVKKsVhZWWFlZYVer6euuVQbZrNZBXbX1taYmZkh\nk8lw//59Njc31YYr/k0HBwfqPQtjIeX1kUiEYrGoqiQFUEpLMfkSNkjSNSsrK6ytral7L6yICKa9\nabFH/fAGHPAQdMmYPh68eFON3kpIOMpAC1PiZYrl3kg1aaVSodfrqTGraZoqwBAQIayyaMmk36CI\n18Gd4+JG7/XckvSdjBupToSHze4//vGPMzc3x/7+Ps1mk+eee07plsSfTMCLVNfatk0qlToCuKTh\nuM/nU1rIYDBIMplU2q9qtarMYsWvzAuoxBew2+2yvLysKjrFQFhYVylQWF1dVd553rY/whSn02kV\ngKyurgKooiFN01RhjLfBtjRQFwmB1+9NMIQ8X1LCyWQSn8/H+vo6lUpFjQUvCJNx9tMejwQAg5PL\n7L1RykmPlUkhPjyyGUk+2aU7H3aNl0Gvm27kmQ5H6HZHarOxhq4H2J137xAJRbEtnQF9CqkEfl+I\n2zeXaDo2Kxtr1DZWSYYiBIJx7izdIRgM09iuYfiC+EJRdu6vc391jX/wuc/x4OZN5uZP84f/+l9w\nfuEsr/7wBrOnx7F1k4X5cR5sbBLQXR+xRCIB2IRiMYLRMLcfLFGulvjjP/+3XLvzJth9djeW+dM/\n+VMYjejbI3yGzp17d5nM5NGwadZqvPTSS0RDYWqVKhsbG4yPjzMxMUE8EWV3e4dSaZ/FxUU6rSa7\nu7uMjY1xcHDgGrCWDqg3G8ycPu2a2u3sc/vWLRqNBr/2a7/G+sYG6xurru7LstF1jatXr7J/sMvZ\nMwuHnQeC9LvuBnD+8cfIZwtsb29jDYaUDg749Kd+mWq1yvdfe52RZaNrYBoGo5FL/w4HfQwgoOlE\nQ2FCPkeBa6kwKxQKJOIp1VLK3NwkEo4RT6cJpTP0+n1GaKQOhay6abB05zYmDvFYlLF8gfJBiVQi\nSavdoNfpH7bLeTQ0YPD+NPdJaZTjTJd8l/nh3XSOm7XKxiKLkzxOvHACgYDa0IvFoqqM3NraAtxK\nLnGhlohe0nu2bbO7u8vy8jKPP/44uq6TTCZZW1tTjM6lS5fo9XrMzMywsrKiNCmiFRFBrfR0/P3f\n/302NjZUBdZXvvIVNcdFGH/37l2i0SiAaprtdeUWB3NZO8bGxqjVasoJvlqtqjEn10AazkuVphhP\n1mo1JX5uNpu88847WJalmhp7HfMXFhZUWlL0YBMTE2xsbLC2tqYsO2TRFwG/pKkEMIv+Jp1Oq/ZF\n4s0kffVEcyOif/F6C4VCSmMkraK8KWivC/qjogMDjrwXr12EHAKYhDUBjui4vI+Ta+rdd47Pq+Og\nyztXvKJtsTGwLLeRvOgFvSBQrmW/31dpeWnqLoUS+Xye9fV17t+/z87OjjJJPX/+vNIRRqNRyuWy\nAuOi3ez3+1y7do2NjQ18Ph/ZbJbV1VU1n6PRqGrdYxiGqpqVRtiAyi6VSiW1RkjqVeZiuVymXq8f\nAWXSS1G0V14iRDzxwAVV4lcmoE8AVzQaJZVKKRAUCAQUeyeCfWG3vPPCm9K1LEsBQ3hYnR0MBpUX\nmgSEAvgEnE9MTJBMuoH49vY2rVaLYDAIPGz35iV6ftrjkQBgx2ljObxRy8OU4tF0itDlXidvWXC9\nF9nLpNldG58vQLvbw0FDG1oMbA1rMCBoBoiFkwSDcbo9g0FAp9droGkGjxUn+OIPXmXkNxjWLaqB\nPsX5Hh3g7r0l8plx0tkM/nAIy4GDcolmu8VHXnyRWMBPwHJolEr84//yNzl78TLbpQpP/eIv8c7N\n2/ybL/87Xv7hD5ianiAWDREMh+gPB7QqNXqVBrcr7xDVbCwDvvTHX8Qw/VjoGKbOaGRx78EK66tr\nPPXkFT72kef5f1/5K269c51/9N/81xim26g8HouxublBPBohkZym2+uQTCbZWFtlZcVd9C994Eli\niRj3DiPwSCTCk1cuEw4FGC+Osb+/j2lAKpGg1+sxMTHO/t4Oly4+znB4Fp/PYGJigm6ry9zcHK1W\ni2azja7DsN9h+d4epmnyra//BblcjtNnFqh3b9Du9hiODvV6joMP8BkafkPDxIbRiHyxQLFYJJyI\n4Q8FWVnfYH4hRCQZ59vf/kvOnTvPfrXK7KWL9PtDiqkUvkiEWqvJcNTn+rWrZBNRYvPzjEbuuAiP\nFd00qRMlZPqV1uPnfRwf73J4vW68//cuDCrQ8Fb+esrevV458n8vQJNIH1Cb8f7+vorg5TwiyF1d\nXaVSqRxpvSIbjHwOiYgFbJw5cwbTNN1ijnicCxcuMD09Tb1eZ3Jykq9+9at0u122trZUKk3uiwjd\nG42G2gjffvvtI0aXjuNQr9fZ29ujUqkwMTHB/fv3MQyDS5cuKT2LZVlUKhWmpqbUxqhpmur5Vq/X\nmZiYUIyctFIaHx9nZmZGsVLSK04i7U6nw/j4uNLQyKYmomER8TuOo1KeslGGQiG1SXhTV8ARtlLu\njTct2+l0VJGAaNQcxyGZTKrNWvpsymv6fD5lXCtFFN40mZiI/ryPk+QyJ+m/5HfvHPKyxvK842yZ\nPM8LvLyHd5MHlAu8VNAJmBoMBmxvbyt9mPf9CeCQopRqtXqkclL89vb39+l2uwrAdDodVlZWFCAR\nUCieXM1mk8FgwKuvvqrWMEmTS/cGYa7Er0/ucTqdViyweJWJD5iwUSLS39raOgJuBTh6x4dcV68x\nscylUqmkqg41TVOPEWZOxroEOWKQKoGDsMfvNS5EoylaOqlgFjZY1kZAadfk2kSjURVM7e/vK3sX\nr+zJK77/Wci3HgkA5mgamq5z2HvF/ZuH6vNuh96JJf4/3lSk6CqEZvcicRm0pu7DGjloAYN+zyIR\nD0F3QCiexG8N2NnZpWfDTrVFKBHDCruL10Fni6lsjnc2DtBN6Oiaq7eyO4fGlDYmNp1mjcnxBE9c\nfhxDd3jnrbfotupEAn6mTp1mfHKM3nDAL33iYzQ1nSeuXOblb/4lpj3Ebxq0ux2GwwGGY+PXDdA0\n/OEw/XYPywZT17EHIzTdjbwsx2a3dEAhmeTr3/omv/arv8Le3h4v/dIv8vWvf52PfOQjbO5sUizm\nKR+UWFm+j2WPSMVjREJhxotFxgoFytUq8UyKtY115ufnyBZcPcONa9dJxFPS/4cAACAASURBVGN0\nmi1m505z794ddnZ2iMfjNOpVksnkoYEfnD17FtM02dnfYenBEq1Wi0gkhoEbgUYSbuT24Reex3Ec\nfvjmDRqHjKVz+KWDGug+n4ahg2mYynHatm2GtsXk9DQzp2fx+/184pOfwhcM0eq0CYYiBIIaumnQ\n7nXx+/3cvXebUMAk5A/RGPbwxV2Xb5/po9/roGG7fUCBkfbz10WexAjLcVyUL4+TxcVLj3sZguNR\nvJxLGGD5XfRL4tYuNhICGGSOiRmppN729/eBhw7ikloRlimXy3HlyhXOnj2LYRisrKwoT6zx8XES\niQQXLlxQFhblctk1Bva0a5HPJJ/XO6+9gZnoch48eEC32+XXf/3X6ff7BINBbt68ycTEBMFgkELB\n1Sa+9dZbKsoXFkuAUCwWo9PpHFq8uJ5nUqUlLNru7i57e3v0+33S6TS5XE5F16L3KpVKlEollYo0\nDOOIuLtQKDAajVhbW1PWD8fvsRdMi4BfuhmI9qVYLBIIBMjn88pcVsTOojsaDofs7OwoNlN0MhL0\njkYj/H6/qqg8vun9PA5v+lB+l8Or55LDq2uUz+w9l2z2x9P3XsbPO1ck4Jc5IAUPMt4mJyfRdV31\n9mw2m6pHo+M4dDodut2uAuC9Xk8BMAEijuOws7OD4zhMTLht3yT9WCqVqNfryhRV5pjXVFXS1Y7j\nKGnASXM9kUiocw4GA1WVuLu7q8CPgCjxLgsGg6ovqKQQJVAQ3agwq8JKeZt5AwoQRSIRlf6T3qYC\n9sRmQ5zq4/E4iURCfT7vffTeSyl6EOAqc8XrBybv1ZuOl9eQNmGSUpXKVsEScFRH+LM4Hg0ApmvY\n2mEkLoqww991R8PwUMwyiLrdrqLMvQuLpAk0TVObkUSRIsKzRw4OJn1rhG766La67iQI2FCuMl6Y\n5H/86lf5xHMfYVju8n/+1bdpA76Qxgcfv8SirXFre59+wMEK+chHc0QDIfT+iN2DHdLFPL/xq5+i\nOD7ORCaBEY2wtTokncsycXqGYCpObnICPRbA0A2CWpCXPvQs165d4+3XX+PKs8/z4M4yY1PjtJww\nNH10Oy3QTEw9QN+2wRmBNQQTDNOk1e1RqTxAs0b8H3/yJ/zCB59l1Gow7Hb43nde5ey5Rfb3d/H7\nTZ548jL1ep0HS/fodlqsrSwzGLg5fjY3CCdiGKZFt+VG/LFIFHswpN6oUdpzmJoY48oTl/jWt77l\ntlip1YhGo8zOzqoFYmpmkgcPHpDKTGKabr/GeDTB2sY6sUSUpftL7G7v0ejrGLqPgT1C13RMHDTH\nRsfGMEDDwTQ0Ts1MMz09TTQRJxJP0BtZDG2HQCSCZugkcjlKpZJLM4cCmMEQI9uitLpH6WCPYaPJ\nYNDBCgUwTNCcEcVsxm38nEwwGB620InYBB7BxsPeTeCktIssShIlehmT4+c6rhWDHxf4y0InegrD\nMPjWt77F5OQkxWKRl19+WfVsPHXqFLOzs1y/fl2lFcbHx5Wdg+innn76aS5evKii71AoxOOPP85T\nTz3FcDhkenqadNo17f30pz+tqvFee+01ZmZm2NvbU+JdYdm8G653c5TF9caNG1y/fp1KpcLTTz/N\nvXv3mJubU1oPEbgXCgWGwyH1ep1Go6E2Dlmgw+Gw6i0pa4tsmJqmuczsYX84ASuyqcoGEI1G2d3d\nVTo5TdPw+/1sbGxQLpfdtl/drnLmFj2W997L+fx+P2fPnmViYoL5+XmSyaTS4Eh/zmq1qpgZua6d\nToetrS1WVlbU/fWmNaXNijAxwqA8CgyYFyjBjxecyNj1Pt6bhvQWqggQkeBdzi2sove6ez2jBEC0\n220qlQqJRIJ0Ok0wGFQ9F8VZXtzvJYDodDqqSbq3Ak/Sxz6fTzG2AqxM02RiYgKfz0csFmNtbY1a\nrcbi4iKFQkE1e/f2kZTr4b1WAizr9Trr6+tEo1FVNFOr1XjttdfUWJfPK+lUGSfyXvx+v7qO3pSs\nZKN2dnYA1FjMZDLKakVAkMiGpPBArFDk80hx1fb2Np1Oh3q9rtYsr7ea3B8BbJ1ORwUR3W5X9WQO\nBAKMj48fkSslEgmlgzQM1zVfqq9lDEhqV0Dt+0lC/jbHIwHALEzl+aVxLPLXoO9YrleYLhUrOkF/\nHAcLNI2hZWEY7oKrmQb9wRCLwyjFMNBFQGdbaI7OQLPo97tE/EF0TWdgmfS7FoNOA6veYNBq0dRD\nfPfeChkjSCEcpznqMjA1rr5xDR8uU1Ntw16pyZOzFwn6dCYLKRYWfoW+5aAHk+QKEzhAubZFLJfm\nox/9GAfVKpoWZGL+HJYZJByL0ml1+eXf+Pv85fe+w2A0YPneO1Srddq9KnNnF+m3GvTo0u+7qQDd\n0LA1G7/hY9AfoBk6oaBJZa9GPpngrddfY+fefSZzOT7wwWdodtzB7QsEeOa5D/Hd736HVCrFxMwp\nxnJ59rd2aPY62BrU63V0zaTRaGFbFvF4nGgoSCIWQ3Ng72CfWCLBy3/xNVLpNONT44xNubYCaDYh\nn5/8eJFSvUo2W2TQ6xIJ+LG6Xa69dZWRDdlcgempOXyBOO8urWHRx6+bBEyNoN9Hv9UmZmiYhkHY\nNJmbnOb09AynT88RiETJT58mXRhn6DhUalVarRa67mN6YREjHKDdbNBqVum22wwbFXKREGYkSMhn\nYg1HDAc9LMem1+ui6zF3obYtet0+A2uEr/tolNzDyWwXcGSh9bIix4HaSdHvSdoxb/pOFjlvCiCd\nTmOaptJcSbrFNE329/cVUyZRdz6fJ5/PqzJ1aV0kon6XGY0wOTmpzi9l8RIovfDCC5RKJTY3NxUg\nqVQqFAoFotHokVYv3lSsABdJGaRSKer1Oqurq0SjURVxS9VXOp3m7t27yuPMNE21wR5nFCS14TiO\nYgUErG1sbNDv95mamlKpO2FKxFpAGgFrmqa8zw4ODggGg2SzWcWQyWeRzc6bUpbGw9lsVvXYGxsb\nI5lMqo1rc3OTUCikbDmE1ajX66rdktePSsaTZBUE/IqruzR6fhQOb3ZEjpNYYvm/jA3vxu1NCXrP\n4Q1EvCl5rzjdWzXXbrfVJt5oNDg4OKDX6ykwLPdQ1/UjWicvMyeWLwIgACYmJlTqT45oNEogEHC1\ntJbF1NTUEQ2UMGnHmUF5z8Jq3rt370jTeDm3gGxv9wNJqcp7qdVq6j1LUCJzRHRWmYzb1s6yLJrN\npgpqvFkpr8+csMAChAaDAa1WS6UppZOFgDxvUOk4jpI7DIdDZRjb6/Vc8iAWU2770p1jMBioCmBh\n+mUeymvKfRdW3Hu/fpbHIwHAHE0HTVNfmqa51hNo2ICugeP+hHiDjWwLQ0dFg6PRCE3XsQ4N1XyS\nptA0BsMhIb/LinX6PWzdzXSORiMMXcd2wPQF6DUa+Ew/B6UKwWicmjVk5cEqQdNBMzX+3t/5O+yt\nbrG5vYNTLhG1LFqVBpOT05yZnUJ3eiyvPODCpScZOGEmxqdY295k4fHHufXOdbLFIjulCramE4um\n6DgW9WYHy7HRg37+q9/6hyT+/f/FP/83/zsz01Osrm2QikVpRKLUqzU0zUE3TXAsDF1H9/kxNIdw\nMIQ1GjE/PcVzzzxNbXuHgGFSLObZ2toimcnS6/WYW1yg1ety4dJlDkr7FItFus0OE9PT7JVL+IMB\nziycZWNrA+oag/7Q3Qwcm2vXrqGjEYxG6A+HzM7Oks5kcLA5fWrWbeVQLhPxh+m0Wpimn267Q7/f\nZf3BMs7IIhqO0Gi22d3ZYWl5lVZ3SBfXAg1Ac0xGvS4+wBo5YI0wAgEmpmawHI1rN98lncmRnJih\n1uqgmT6Gjk5v5JBMRsBn0up10Q2dWqWCMxxwbmEeZ2RhjQY0a3UMTce2LHTNxjQN1SrHrx9GjJaO\n7TwaVZCyUZyUhjzp8G46Aoi8kZtYDMhj5fD6HsnryGYlpdySxqpUKirKPHXqFGfOnFGCcXm83+8n\nmUwyOTlJIBCg2WySzWaVkammudYV1WqVdDqttIaS+hQAlsvl+MxnPkOr1eIP/uAPVANpWWTL5bKK\nwr0pB2EgAoEA58+fV+kOuS5e7aiUpM/Ozqpzi9u8F4RINZWcQ1IvW1tbSsMyMTGhKieTyaRKewhj\nJnIIKfuXjVeidfF7OskuQT5bLBYjHo8zPj4OoApohPUUHyUvE+htLCxVel7ndWE4hJ2Qcn4BbgIe\nHrXjONA4/rN33pwEvo7ri72HsGHHU6+ih5MUnKTMwE1Li1BcghcZdwJQ5Np7232JLYjcC9FAJZNJ\nCoWCAiSG4XYkEZZpfX2dXC6nUvwiVj9+XQTYSTpOimYkuAoEAnQ6HTWOJR23sbGhxoF81na7rYIb\nwzBUIYC8PqDWmVarxe7uLoBqKC+AS+aCzJHRaORKQjwedGKbIqlbb6Al90aCPqmUFAAnnzccDiut\naiaTOdJuqVqtKobetm3VNuz4GDtpnAhj+tMejwYA8wArzXlovupoD79rmqsR03QdDfth/tbvx7Jt\nbBuw3W7ljuNgSTri8KJ1B+4kMUwTzbZxHLB1Hct20DXANAjFo9TX3X51ps/H7d11ilM5ktEwC7On\nyeVyXHrsIvt7ZRKpHLV6g4sfeBJ/TKOQS7O2cZ/pi1fY3zugUJyk3G6QzuXYK5cpzp+lP2iSOz3D\nwvkF/vIbL/PUh55lcnycRqfNcNjnzIXz/KOZaUwzzP/2xS8ymSny/e+9ztjEJKYvyLA/wG+YDPs2\n6BpmLMbT5z7IoNVif+UBH33xw+ijIZnpSSKhMLNnZ0lki2RyeTa2t7j29jvEEnES6RSzcwtUyxX0\nYJihpvPYxUvs7e2xvr7O0LLJ5PLuJphJ82B5idNzZwiFQmxtbeH3+3ns/AUqtTLJZJLd3V263S5j\nhXG6rS71Wpux6XE0zQF7xOz0FM7IYunBMrV6k0qlxGg4xNTAB9iOe8ft4QgfkI6HGQ2H5AsFHr90\nkaFhEkjE+YUrH6BQLDI9v0i926Xd7fH/fPubrK6vEYmEcLBotZoszs5ycf4MZjBIvd7EGQ0JhvxE\n4jEGXbdgQ9MdhqMR/oCb0un2Og8BhPFITAt1vFek743WvSkY6TsoYMsr2vYuJt5KIq/g2Lu4SNWf\npEN6vR6BQIBCocDc3Bzj4+PMz8/j9/uZnp5G0zROnTpFoVBQ1UfFYlGltXRdJ51OMxqNyGaz+P1u\nelp8sObn59XzLMtibGyMj370o3zta19TmrHd3V2lq1IVzIefLZVKqcW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DA0KhEIVCgaee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\n7dFsNplMJ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LCTnyW/exhULSwgNf2NiuKFPAp9/MkEFQRMhgO6jRqRP8G1bVK2RS6bxrVs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\njemgobPHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a880c15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  7.99014142537e-09\n",
      "dw error:  2.67832819804e-10\n",
      "db error:  3.88351923299e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param) #out=n*filter_num*f**2\n",
    "dx, dw, db = conv_backward_naive(dout, cache) #dx_cols=(w**2 * C)*(f**2*n)  dw=(w**2*C)*filter_num db=filter_num\n",
    "\n",
    "# Your errors should be around 1e-8'\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 0.017392s\n",
      "Fast: 0.013733s\n",
      "Speedup: 1.266380x\n",
      "Difference:  0.0\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 0.074265s\n",
      "Fast: 0.013051s\n",
      "Speedup: 5.690562x\n",
      "dx difference:  0.0\n",
      "dw difference:  3.9350463109e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.003163s\n",
      "fast: 0.003063s\n",
      "speedup: 1.032851x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.011550s\n",
      "speedup: 1.058050x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  9.59113262192e-09\n",
      "dw error:  5.80239113733e-09\n",
      "db error:  1.01463434118e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.52186199803e-09\n",
      "dw error:  2.7020226461e-10\n",
      "db error:  1.45127239359e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('W--: ', 32)\n",
      "('filter_size--: ', 7)\n",
      "('outw--: ', 32)\n",
      "('a: ', (50, 32, 32, 32))\n",
      "('out: ', (50, 32, 32, 32))\n",
      "('out_max--: ', (50, 32, 16, 16))\n",
      "('W2--: ', (8192, 100))\n",
      "Initial loss (no regularization):  2.30258686694\n",
      "('a: ', (50, 32, 32, 32))\n",
      "('out: ', (50, 32, 32, 32))\n",
      "('out_max--: ', (50, 32, 16, 16))\n",
      "('W2--: ', (8192, 100))\n",
      "Initial loss (with regularization):  2.50857889475\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to 1e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('param is: ', 'W1')\n",
      "W1 max relative error: 1.380104e-04\n",
      "('param is: ', 'W2')\n",
      "W2 max relative error: 1.822723e-02\n",
      "('param is: ', 'W3')\n",
      "W3 max relative error: 3.064049e-04\n",
      "('param is: ', 'b1')\n",
      "b1 max relative error: 3.477652e-05\n",
      "('param is: ', 'b2')\n",
      "b2 max relative error: 2.516375e-03\n",
      "('param is: ', 'b3')\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    print(('param is: ', param_name))\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    #print(('analysis gradiend is %s' %(param_name), grads[param_name]))\n",
    "    #print(('numerical gradiend is: ', param_grad_num))\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.414060\n",
      "(Epoch 0 / 20) train acc: 0.200000; val_acc: 0.137000\n",
      "(Iteration 2 / 40) loss: 3.102925\n",
      "(Epoch 1 / 20) train acc: 0.140000; val_acc: 0.081000\n",
      "(Iteration 3 / 40) loss: 2.673266\n",
      "(Iteration 4 / 40) loss: 2.257389\n",
      "(Epoch 2 / 20) train acc: 0.140000; val_acc: 0.095000\n",
      "(Iteration 5 / 40) loss: 1.967534\n",
      "(Iteration 6 / 40) loss: 1.914532\n",
      "(Epoch 3 / 20) train acc: 0.400000; val_acc: 0.163000\n",
      "(Iteration 7 / 40) loss: 1.903067\n",
      "(Iteration 8 / 40) loss: 2.085949\n",
      "(Epoch 4 / 20) train acc: 0.420000; val_acc: 0.197000\n",
      "(Iteration 9 / 40) loss: 1.566363\n",
      "(Iteration 10 / 40) loss: 1.634450\n",
      "(Epoch 5 / 20) train acc: 0.530000; val_acc: 0.184000\n",
      "(Iteration 11 / 40) loss: 1.140067\n",
      "(Iteration 12 / 40) loss: 1.146590\n",
      "(Epoch 6 / 20) train acc: 0.630000; val_acc: 0.205000\n",
      "(Iteration 13 / 40) loss: 1.205710\n",
      "(Iteration 14 / 40) loss: 1.097082\n",
      "(Epoch 7 / 20) train acc: 0.660000; val_acc: 0.204000\n",
      "(Iteration 15 / 40) loss: 0.676990\n",
      "(Iteration 16 / 40) loss: 0.854177\n",
      "(Epoch 8 / 20) train acc: 0.760000; val_acc: 0.194000\n",
      "(Iteration 17 / 40) loss: 0.965628\n",
      "(Iteration 18 / 40) loss: 0.449211\n",
      "(Epoch 9 / 20) train acc: 0.820000; val_acc: 0.167000\n",
      "(Iteration 19 / 40) loss: 0.475107\n",
      "(Iteration 20 / 40) loss: 0.495566\n",
      "(Epoch 10 / 20) train acc: 0.860000; val_acc: 0.197000\n",
      "(Iteration 21 / 40) loss: 0.440097\n",
      "(Iteration 22 / 40) loss: 0.180259\n",
      "(Epoch 11 / 20) train acc: 0.910000; val_acc: 0.228000\n",
      "(Iteration 23 / 40) loss: 0.253805\n",
      "(Iteration 24 / 40) loss: 0.546616\n",
      "(Epoch 12 / 20) train acc: 0.950000; val_acc: 0.231000\n",
      "(Iteration 25 / 40) loss: 0.182069\n",
      "(Iteration 26 / 40) loss: 0.162158\n",
      "(Epoch 13 / 20) train acc: 0.970000; val_acc: 0.231000\n",
      "(Iteration 27 / 40) loss: 0.075110\n",
      "(Iteration 28 / 40) loss: 0.076801\n",
      "(Epoch 14 / 20) train acc: 0.950000; val_acc: 0.194000\n",
      "(Iteration 29 / 40) loss: 0.094693\n",
      "(Iteration 30 / 40) loss: 0.226416\n",
      "(Epoch 15 / 20) train acc: 0.990000; val_acc: 0.208000\n",
      "(Iteration 31 / 40) loss: 0.038042\n",
      "(Iteration 32 / 40) loss: 0.057360\n",
      "(Epoch 16 / 20) train acc: 0.960000; val_acc: 0.210000\n",
      "(Iteration 33 / 40) loss: 0.125637\n",
      "(Iteration 34 / 40) loss: 0.073974\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.223000\n",
      "(Iteration 35 / 40) loss: 0.021914\n",
      "(Iteration 36 / 40) loss: 0.006250\n",
      "(Epoch 18 / 20) train acc: 0.990000; val_acc: 0.234000\n",
      "(Iteration 37 / 40) loss: 0.063614\n",
      "(Iteration 38 / 40) loss: 0.015547\n",
      "(Epoch 19 / 20) train acc: 0.990000; val_acc: 0.235000\n",
      "(Iteration 39 / 40) loss: 0.014208\n",
      "(Iteration 40 / 40) loss: 0.108470\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.224000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=20, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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q80Xk2HT2jslEVzgQERHN1C8iMTTdMdk0SL/pjkmgw1CmACYi0nnd4i5LEUku3TEpIpJc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\ntE6TeYAUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58d0eaf908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304805\n",
      "(Epoch 0 / 1) train acc: 0.095000; val_acc: 0.102000\n",
      "(Iteration 21 / 980) loss: 2.150357\n",
      "(Iteration 41 / 980) loss: 2.273119\n",
      "(Iteration 61 / 980) loss: 2.063232\n",
      "(Iteration 81 / 980) loss: 1.742023\n",
      "(Iteration 101 / 980) loss: 1.981319\n",
      "(Iteration 121 / 980) loss: 1.732675\n",
      "(Iteration 141 / 980) loss: 1.915589\n",
      "(Iteration 161 / 980) loss: 1.673976\n",
      "(Iteration 181 / 980) loss: 1.939160\n",
      "(Iteration 201 / 980) loss: 1.726705\n",
      "(Iteration 221 / 980) loss: 1.920065\n",
      "(Iteration 241 / 980) loss: 1.701364\n",
      "(Iteration 261 / 980) loss: 1.716942\n",
      "(Iteration 281 / 980) loss: 1.805717\n",
      "(Iteration 301 / 980) loss: 1.572577\n",
      "(Iteration 321 / 980) loss: 1.443798\n",
      "(Iteration 341 / 980) loss: 1.382261\n",
      "(Iteration 361 / 980) loss: 1.466711\n",
      "(Iteration 381 / 980) loss: 1.558932\n",
      "(Iteration 401 / 980) loss: 1.250166\n",
      "(Iteration 421 / 980) loss: 1.668563\n",
      "(Iteration 441 / 980) loss: 1.526688\n",
      "(Iteration 461 / 980) loss: 1.551481\n",
      "(Iteration 481 / 980) loss: 1.685146\n",
      "(Iteration 501 / 980) loss: 1.670650\n",
      "(Iteration 521 / 980) loss: 1.438076\n",
      "(Iteration 541 / 980) loss: 1.667533\n",
      "(Iteration 561 / 980) loss: 1.695970\n",
      "(Iteration 581 / 980) loss: 1.665381\n",
      "(Iteration 601 / 980) loss: 1.249202\n",
      "(Iteration 621 / 980) loss: 1.848658\n",
      "(Iteration 641 / 980) loss: 1.327899\n",
      "(Iteration 661 / 980) loss: 1.359921\n",
      "(Iteration 681 / 980) loss: 1.674219\n",
      "(Iteration 701 / 980) loss: 1.386997\n",
      "(Iteration 721 / 980) loss: 1.380962\n",
      "(Iteration 741 / 980) loss: 1.413199\n",
      "(Iteration 761 / 980) loss: 1.388603\n",
      "(Iteration 781 / 980) loss: 1.319042\n",
      "(Iteration 801 / 980) loss: 1.326120\n",
      "(Iteration 821 / 980) loss: 1.520923\n",
      "(Iteration 841 / 980) loss: 1.416424\n",
      "(Iteration 861 / 980) loss: 1.354568\n",
      "(Iteration 881 / 980) loss: 1.161279\n",
      "(Iteration 901 / 980) loss: 1.344773\n",
      "(Iteration 921 / 980) loss: 1.184343\n",
      "(Iteration 941 / 980) loss: 1.387212\n",
      "(Iteration 961 / 980) loss: 1.330453\n",
      "(Epoch 1 / 1) train acc: 0.506000; val_acc: 0.516000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Yhh/rJWinqPbSOBdaC0Pns8sXpmDuXz3Hhjf83sfUdneI/i80sQM3kH27Gv4/Q9v0/N8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\ndcdkRwAEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58d1018908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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